Automated work chart systems and methods

ABSTRACT

The systems and methods provide an action recognition and analytics tool for use in manufacturing, health care services, shipping, retailing and other similar contexts. Machine learning action recognition can be utilized to determine cycles, processes, actions, sequences, objects and or the like in one or more sensor streams. The sensor streams can include, but are not limited to, one or more video sensor frames, thermal sensor frames, infrared sensor frames, and or three-dimensional depth frames. The analytics tool can provide for automatic creation of work charts.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/581,541 filed Nov. 3, 2017, which is incorporatedherein in its entirety.

BACKGROUND OF THE INVENTION

As the world's population continues to grow, the demand for goods andservices continues to increase. Industries grow in lockstep with theincreased demand and often require an ever-expanding network ofenterprises employing various processes to accommodate the growingdemand for goods and services. For example, an increased demand inautomobiles can increase the need for robust assembly lines, capable ofcompleting a larger number of processes in each station on the assemblyline while minimizing anomalies and reducing completion times associatewith each process. Typically, process anomalies are the result of anoperator deviating from or incorrectly performing one or more actions.In addition, variances in the completion times of a process can beattributed to inadequate designs that result in an operator beingchallenged to execute the required actions in the required time. Quiteoften, if the number of actions per station increases either due to anincrease in the complexity of the actions or a decrease in the timeavailable in each station, the cognitive load on the operator increases,resulting in higher deviation rates.

Common quality improvement and process optimization methodologies, foruse by manufacturing organizations, include Toyota's Toyota ProductionSystem and Motorola's Six-Sigma. The optimization methodologies such asLean Manufacturing and Six-Sigma rely on manual techniques to gatherdata on human activity. The data gathered using such manual techniquestypically represent a small and incomplete data set. Worse, manualtechniques can generate fundamentally biased data sets, since thepersons being measured may be “performing” for the observer and notproviding truly representative samples of their work, which is commonlyreferred to as the Hawthorne and Heisenberg effect. Such manualtechniques can also be subject to substantial delays between thecollection and analysis of the data.

There is currently a growth in the use of Industrial Internet of Things(IIoT) devices in manufacturing and other contexts. However, machinescurrently only perform a small portion of tasks in manufacturing.Therefore, instrumenting machines used in manufacturing withelectronics, software, sensors, actuators and connectivity to collect,exchange and utilize data is centered on a small portion ofmanufacturing tasks, which the Boston Consulting Group estimated in 2016to be about 10% of the task or action that manufactures use to buildproducts. Accordingly, IIoT devices also provides an incomplete dataset.

Accordingly, there is a continuing need for systems and methods forcollecting information about manufacturing, health care services,shipping, retailing and other similar context and providing analytictools for improving the performance in such contexts. Amongst otherreasons, the information could for example be utilized to improve thequality of products or services being delivered, for training employees,for communicating with customers and handling warranty claims andrecalls.

SUMMARY OF THE INVENTION

The present technology may best be understood by referring to thefollowing description and accompanying drawings that are used toillustrate embodiments of the present technology directed towardautomated work charting.

In aspects, an action recognition and analytics system can be utilizedto determine cycles, processes, actions, sequences, objects and or thelike in one or more sensor streams. The sensor streams can include, butare not limited to, one or more frames of video sensor data, thermalsensor data, infrared sensor data, and or three-dimensional depth sensordata. The action recognition and analytics system can be applied to anynumber of contexts, including but not limited to manufacturing, healthcare services, shipping and retailing. The sensor streams, and thedetermined cycles, processes, actions, sequences, objects, parametersand or the like can be stored in a data structure. The determinedcycles, processes, actions, sequences objects and or the like can beindexed to corresponding portions of the sensor streams. The actionrecognition and analytics system can provide for automated workcharting.

In one embodiment, a work charting method can include receiving one ormore given indicators or criteria. One or more given data sets can beaccessed based on the one or more given indicators or criteria. The oneor more given data sets can include one or more indicators of at leastone of one or more cycles, one or more processes, one or more actions,one or more sequences, one or more objects, and one or more parametersindexed to corresponding portions of the one or more sensor streams. Arepresentative data set can be determined from the one or more givendata sets. A work chart can be created from the representative data set.The work chart can include a plurality of work elements and dependenciesbetween the work elements.

In another embodiment, an action recognition and analytics system caninclude one or more data storage units, one or more interfaces, and oneor more engines. The one or more engines can be configured to receiveone or more given indicators or criteria. The one or more engines canalso be configured to access one or more given data sets stored on theone or more data storage units based on the one or more given indicatorsor criteria. The one or more given data sets can include one or moreindicators of at least one of one or more cycles, one or more processes,one or more actions, one or more sequences, one or more objects, and oneor more parameters, indexed to corresponding portions of one or moresensor streams. The one or more engines can be configured to determine arepresentative data set from the one or more given data sets. The one ormore engines can be configured to create a work chart from therepresentative data set, wherein the work chart include a plurality ofwork elements and dependencies between the work elements.

The one or more analytics front-end units can be configured to receivinga unique identifier of a given instance of the subject. The one or moreanalytics front-end units can be configured to access the one or moredata structures on the one or more data storage devices including aplurality of data sets mapped to a plurality of unique identifiers toretrieve indicators of one or more cycles, processes, actions,sequences, objects, and parameters indexed to corresponding portions ofthe sensor streams for the given instance of the subject. The one ormore analytics front-end units can also be configured to access the oneor more data structures on the data storage unit including the sensorstreams to retrieve corresponding portions of the sensor streams indexedby the one or more cycles, processes, actions, sequences, objects, andparameters for the given instance of the subject. The analyticsfront-end unit can be configured to output the corresponding portions ofthe sensor streams and the corresponding one or more cycles, processes,actions, sequences, objects, parameters for the given instance of thesubject.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present technology are illustrated by way of exampleand not by way of limitation, in the figures of the accompanyingdrawings and in which like reference numerals refer to similar elementsand in which:

FIG. 1 shows an action recognition and analytics system, in accordancewith aspect of the present technology.

FIG. 2 shows an exemplary deep learning type machine learning back-endunit, in accordance with aspects of the present technology.

FIG. 3 shows an exemplary Convolution Neural Network (CNN) and LongShort Term Memory (LSTM) Recurrent Neural Network (RNN), in accordancewith aspects of the present technology.

FIG. 4 shows an exemplary method of detecting actions in a sensorstream, in accordance with aspects of the present technology.

FIG. 5 shows an action recognition and analytics system, in accordancewith aspects of the present technology.

FIG. 6 shows an exemplary method of detecting actions, in accordancewith aspects of the present technology.

FIG. 7 shows an action recognition and analytics system, in accordancewith aspects of the present technology.

FIG. 8 shows an exemplary station, in accordance with aspects of thepresent technology

FIG. 9 shows an exemplary station, in accordance with aspects of thepresent technology

FIG. 10 shows an exemplary station activity analysis method, inaccordance with one embodiment.

FIGS. 11A and 11B show a method of creating work charts, in accordancewith aspects of the present technology.

FIG. 12 shows an exemplary work chart, in accordance with aspects of thepresent technology.

FIG. 13 shows another an exemplary work chart, in accordance withaspects of the present technology.

FIG. 14 shows an exemplary computing device, in accordance with aspectsof the present technology.

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail to the embodiments of the presenttechnology, examples of which are illustrated in the accompanyingdrawings. While the present technology will be described in conjunctionwith these embodiments, it will be understood that they are not intendedto limit the invention to these embodiments. On the contrary, theinvention is intended to cover alternatives, modifications andequivalents, which may be included within the scope of the invention asdefined by the appended claims. Furthermore, in the following detaileddescription of the present technology, numerous specific details are setforth in order to provide a thorough understanding of the presenttechnology. However, it is understood that the present technology may bepracticed without these specific details. In other instances, well-knownmethods, procedures, components, and circuits have not been described indetail as not to unnecessarily obscure aspects of the presenttechnology.

Some embodiments of the present technology which follow are presented interms of routines, modules, logic blocks, and other symbolicrepresentations of operations on data within one or more electronicdevices. The descriptions and representations are the means used bythose skilled in the art to most effectively convey the substance oftheir work to others skilled in the art. A routine, module, logic blockand/or the like, is herein, and generally, conceived to be asell-consistent sequence of processes or instructions leading to adesired result. The processes are those including physical manipulationsof physical quantities. Usually, though not necessarily, these physicalmanipulations take the form of electric or magnetic signals capable ofbeing stored, transferred, compared and otherwise manipulated in anelectronic device. For reasons of convenience, and with reference tocommon usage, these signals are referred to as data, bits, values,elements, symbols, characters, terms, numbers, strings, and/or the likewith reference to embodiments of the present technology.

It should be borne in mind, however, that all of these terms are to beinterpreted as referencing physical manipulations and quantities and aremerely convenient labels and are to be interpreted further in view ofterms commonly used in the art. Unless specifically stated otherwise asapparent from the following discussion, it is understood that throughdiscussions of the present technology, discussions utilizing the termssuch as “receiving,” and/or the like, refer to the actions and processesof an electronic device such as an electronic computing device thatmanipulates and transforms data. The data is represented as physical(e.g., electronic) quantities within the electronic device's logiccircuits, registers, memories and/or the like, and is transformed intoother data similarly represented as physical quantities within theelectronic device.

As used herein, the use of the disjunctive is intended to include theconjunctive. The use of definite or indefinite articles is not intendedto indicate cardinality. In particular, a reference to “the” object or“a” object is intended to denote also one of a possible plurality ofsuch objects. It is also to be understood that the phraseology andterminology used herein is for the purpose of description and should notbe regarded as limiting.

As used herein the term process can include processes, procedures,transactions, routines, practices, and the like. As used herein, theterm sequence can include sequences, orders, arrangements, and the like.As used herein the term action can include actions, steps, tasks,activity, motion, movement, and the like. As used herein the term objectcan include objects, parts, components, items, elements, pieces,assemblies, sub-assemblies, and the like. As used herein a process caninclude a set of actions or one or more subsets of actions, arranged inone or more sequences, and performed on one or more objects by one ormore actors. As used herein a cycle can include a set of processes orone or more subsets of processes performed in one or more sequences. Asused herein a sensor stream can include a video sensor stream, thermalsensor stream, infrared sensor stream, hyperspectral sensor stream,audio sensor stream, depth data stream, and the like. As used hereinframe based sensor stream can include any sensor stream that can berepresented by a two or more dimensional array of data values. As usedherein the term parameter can include parameters, attributes, or thelike. As used herein the term indicator can include indicators,identifiers, labels, tags, states, attributes, values or the like. Asused herein the term feedback can include feedback, commands,directions, alerts, alarms, instructions, orders, and the like. As usedherein the term actor can include actors, workers, employees, operators,assemblers, contractors, associates, managers, users, entities, humans,cobots, robots, and the like as well as combinations of them. As usedherein the term robot can include a machine, device, apparatus or thelike, especially one programmable by a computer, capable of carrying outa series of actions automatically. The actions can be autonomous,semi-autonomous, assisted, or the like. As used herein the term cobotcan include a robot intended to interact with humans in a sharedworkspace. As used herein the term package can include packages,packets, bundles, boxes, containers, cases, cartons, kits, and the like.As used herein, real time can include responses within a given latency,which can vary from sub-second to seconds.

Referring to FIG. 1 an action recognition and analytics system, inaccordance with aspect of the present technology, is shown. The actionrecognition and analytics system 100 can be deployed in a manufacturing,health care, warehousing, shipping, retail, restaurant or similarcontext. A manufacturing context, for example, can include one or morestations 105-115 and one or more actors 120-130 disposed at the one ormore stations. The actors can include humans, machine or any combinationtherefore. For example, individual or multiple workers can be deployedat one or more stations along a manufacturing assembly line. One or morerobots can be deployed at other stations. A combination of one or moreworkers and/or one or more robots can be deployed additional stations Itis to be noted that the one or more stations 105-115 and the one or moreactors are not generally considered to be included in the system 100.

In a health care implementation, an operating room can comprise a singlestation implementation. A plurality of sensors, such as video cameras,thermal imaging sensors, depth sensors, or the like, can be disposednon-intrusively at various positions around the operating room. One ormore additional sensors, such as audio, temperature, acceleration,torque, compression, tension, or the like sensors, can also be disposednon-intrusively at various positions around the operating room.

In a shipping implementation, the plurality of stations may representdifferent loading docks, conveyor belts, forklifts, sorting stations,holding areas, and the like. A plurality of sensors, such as videocameras, thermal imaging sensors, depth sensors, or the like, can bedisposed non-intrusively at various positions around the loading docks,conveyor belts, forklifts, sorting stations, holding areas, and thelike. One or more additional sensors, such as audio, temperature,acceleration, torque, compression, tension, or the like sensors, canalso be disposed non-intrusively at various positions.

In a retailing implementation, the plurality of stations may representone or more loading docks, one or more stock rooms, the store shelves,the point of sale (e.g. cashier stands, self-checkout stands andauto-payment geofence), and the like. A plurality of sensors such asvideo cameras, thermal imaging sensors, depth sensors, or the like, canbe disposed non-intrusively at various positions around the loadingdocks, stock rooms, store shelves, point of sale stands and the like.One or more additional sensors, such as audio, acceleration, torque,compression, tension, or the like sensors, can also be disposednon-intrusively at various positions around the loading docks, stockrooms, store shelves, point of sale stands and the like.

In a warehousing or online retailing implementation, the plurality ofstations may represent receiving areas, inventory storage, pickingtotes, conveyors, packing areas, shipping areas, and the like. Aplurality of sensors, such as video cameras, thermal imaging sensors,depth sensors, or the like, can be disposed non-intrusively at variouspositions around the receiving areas, inventory storage, picking totes,conveyors, packing areas, and shipping areas. One or more additionalsensors, such as audio, temperature, acceleration, torque, compression,tension, or the like sensors, can also be disposed non-intrusively atvarious positions.

Aspect of the present technology will be herein further described withreference to a manufacturing context so as to best explain theprinciples of the present technology without obscuring aspects of thepresent technology. However, the present technology as further describedbelow can also be readily applied in health care, warehousing, shipping,retail, restaurants, and numerous other similar contexts.

The action recognition and analytics system 100 can include one or moreinterfaces 135-165. The one or more interface 135-145 can include one ormore sensors 135-145 disposed at the one or more stations 105-115 andconfigured to capture streams of data concerning cycles, processes,actions, sequences, object, parameters and or the like by the one ormore actors 120-130 and or at the station 105-115. The one or moresensors 135-145 can be disposed non-intrusively, so that minimal tochanges to the layout of the assembly line or the plant are required, atvarious positions around one or more of the stations 105-115. The sameset of one or more sensors 135-145 can be disposed at each station105-115, or different sets of one or more sensors 135-145 can bedisposed at different stations 105-115. The sensors 135-145 can includeone or more sensors such as video cameras, thermal imaging sensors,depth sensors, or the like. The one or more sensors 135-145 can alsoinclude one or more other sensors, such as audio, temperature,acceleration, torque, compression, tension, or the like sensors.

The one or more interfaces 135-165 can also include but not limited toone or more displays, touch screens, touch pads, keyboards, pointingdevices, button, switches, control panels, actuators, indicator lights,speakers, Augmented Reality (AR) interfaces. Virtual Reality (VR)interfaces, desktop Personal Computers (PCs), laptop PCs, tablet PCs,smart phones, robot interfaces, cobot interfaces. The one or moreinterfaces 135-165 can be configured to receive inputs from one or moreactors 120-130, one or more engines 170 or other entities. Similarly,the one or more interfaces 135-165 can be configured to output to one ormore actors 120-130, one or more engine 170 or other entities. Forexample, the one or more front-end units 190 can output one or moregraphical user interfaces to present training content, work charts, realtime alerts, feedback and or the like on one or more interfaces 165,such displays at one or more stations 120-130, at management portals ontablet PCs, administrator portals as desktop PCs or the like. In anotherexample, the one or more front-end units 190 can control an actuator topush a defective unit of the assembly line when a defect is detected.The one or more front-end units can also receive responses on a touchscreen display device, keyboard, one or more buttons, microphone or thelike from one or more actors. Accordingly, the interfaces 135-165 canimplement an analysis interface, mentoring interface and or the like ofthe one or more front-end units 190.

The action recognition and analytics system 100 can also include one ormore engines 170 and one or more data storage units 175. The one or moreinterfaces 135-165, the one or more data storage units 175, the one ormore machine learning back-end units 180, the one or more analyticsunits 185, and the one or more front-end units 190 can be coupledtogether by one or more networks 192. It is also to be noted thatalthough the above described elements are described as separateelements, one or more elements of the action recognition and analyticssystem 100 can be combined together or further broken into differentelements.

The one or more engines 170 can include one or more machine learningback-end units 180, one or more analytics units 185, and one or morefront-end units 190. The one or more data storage units 175, the one ormore machine learning back-end units 180, the one or more analyticsunits 185, and the one or more analytics front-end units 190 can beimplemented on a single computing device, a common set of computingdevices, separate computing device, or different sets of computingdevices that can be distributed across the globe inside and outside anenterprise. Aspects of the one or more machine learning back-end units180, the one or more analytics units 185 and the one or more front-endunits 190, and or other computing units of the action recognition andanalytics system 100 can be implemented by one or more centralprocessing units (CPU), one or more graphics processing units (GPU), oneor more tensor processing units (TPU), one or more digital signalprocessors (DSP), one or more microcontrollers, one or more fieldprogrammable gate arrays and or the like, and any combination thereof.In addition, the one or more data storage units 175, the one or moremachine learning back-end units 180, the one or more analytics units185, and the one or more front-end units 190 can be implemented locallyto the one or more stations 105-115, remotely from the one or morestations 105-115, or any combination of locally and remotely. In oneexample, the one or more data storage units 175, the one or more machinelearning back-end units 180, the one or more analytics units 185, andthe one or more front-end units 190 can be implemented on a server local(e.g., on site at the manufacturer) to the one or more stations 105-115.In another example, the one or more machine learning back-end units 135,the one or more storage units 140 and analytics front-end units 145 canbe implemented on a cloud computing service remote from the one or morestations 105-115. In yet another example, the one or more data storageunits 175 and the one or more machine learning back-end units 180 can beimplemented remotely on a server of a vendor, and one or more datastorage units 175 and the one or more front-end units 190 areimplemented locally on a server or computer of the manufacturer. Inother examples, the one or more sensors 135-145, the one or more machinelearning back-end units 180, the one or more front-end unit 190, andother computing units of the action recognition and analytics system 100can perform processing at the edge of the network 192 in an edgecomputing implementation. The above example of the deployment of one ormore computing devices to implement the one or more interfaces 135-165,the one or more engines 170, the one or more data storage units 140 andone or more analytics front-end units 145, are just some of the manydifferent configuration for implementing the one or more machinelearning back-end units 135, one or more data storage units 140. Anynumber of computing devices, deployed locally, remotely, at the edge orthe like can be utilized for implementing the one or more machinelearning back-end units 135, the one or more data storage units 140, theone or more analytics front-end units 145 or other computing units.

The action recognition and analytics system 100 can also optionallyinclude one or more data compression units associated with one or moreof the interfaces 135-165. The data compression units can be configuredto compress or decompress data transmitted between the one or moreinterface 135-165, and the one or more engines 170. Data compression,for example, can advantageously allow the sensor data from the one ormore interface 135-165 to be transmitted across one or more existingnetworks 192 of a manufacturer. The data compression units can also beintegral to one or more interfaces 135-165 or implemented separately.For example, video capture sensors may include an integral MotionPicture Expert Group (MPEG) compression unit, (e.g., H-264encoder/decoder). In an exemplary implementation, the one or more datacompression units can use differential coding and arithmetic encoding toobtain a 20× reduction in the size of depth data from depth sensors. Thedata from a video capture sensor can comprise roughly 30 GB of H.264compressed data per camera, per day for a factory operation with threeeight-hour shifts. The depth data can comprise roughly another 400 GB ofuncompressed data per sensor, per day. The depth data can be compressedby an algorithm to approximately 20 GB per sensor, per day. Together, aset of a video sensor and a depth sensor can generate approximately 50GB of compressed data per day. The compression can allow the actionrecognition and analytics system 100 to use a factory's network 192 tomove and store data locally or remotely (e.g., cloud storage).

The action recognition and analytics system 100 can also becommunicatively coupled to additional data sources 194, such as but notlimited to a Manufacturing Execution Systems (MES), warehouse managementsystem, or patient management system. The action recognition andanalytics system 100 can receive additional data, including one or moreadditional sensor streams, from the additional data sources 194. Theaction recognition and analytics system 100 can also output data, sensorstreams, analytics result and or the like to the additional data sources194. For example, the action recognition can identify a barcode on anobject and provide the barcode input to a MES for tracking.

The action recognition and analytics system 100 can continually measureaspects of the real-world, making it possible to describe a contextutilizing vastly more detailed data sets, and to solve importantbusiness problems like line balancing, ergonomics, and or the like. Thedata can also reflect variations over time. The one or more machinelearning back-end units 170 can be configured to recognize, in realtime, one or more cycles, processes, actions, sequences, objects,parameters and the like in the sensor streams received from theplurality of sensors 135-145. The one or more machine learning back-endunits 180 can recognize cycles, processes, actions, sequences, objects,parameters and the like in sensor streams utilizing deep learning,decision tree learning, inductive logic programming, clustering,reinforcement learning, Bayesian networks, and or the like.

Referring now to FIG. 2, an exemplary deep learning type machinelearning back-end unit, in accordance with aspects of the presenttechnology, is shown. The deep learning unit 200 can be configured torecognize, in real time, one or more cycles, processes, actions,sequences, objects, parameters and the like in the sensor streamsreceived from the plurality of sensors 120-130. The deep learning unit200 can include a dense optical flow computation unit 210, a ConvolutionNeural Networks (CNNs) 220, a Long Short Term Memory (LSTM) RecurrentNeural Network (RNN) 230, and a Finite State Automata (FSA) 240. TheCNNs 220 can be based on two-dimensional (2D) or three-dimensional (3D)convolutions. The dense optical flow computation unit 210 can beconfigured to receive a stream of frame-based sensor data 250 fromsensors 120-130. The dense optical flow computation unit 210 can beconfigured to estimate an optical flow, which is a two-dimension (2D)vector field where each vector is a displacement vector showing themovement of points from a first frame to a second frame. The CNNs 220can receive the stream of frame-based sensor data 250 and the opticalflow estimated by the dense optical flow computation unit 210. The CNNs220 can be applied to video frames to create a digest of the frames. Thedigest of the frames can also be referred to as the embedding vector.The digest retains those aspects of the frame that help in identifyingactions, such as the core visual clues that are common to instances ofthe action in question.

In a three-dimensional Convolution Neural Network (3D CNN) basedapproach, spatio-temporal convolutions can be performed to digestmultiple video frames together to recognize actions. For 3D CNN, thefirst two dimension can be along space, and in particular the width andheight of each video frame. The third dimension can be along time. Theneural network can learn to recognize actions not just from the spatialpattern in individual frame, but also jointly in space and time. Theneural network is not just using color patterns in one frame torecognize actions. Instead, the neural network is using how the patternshifts with time (i.e., motion cues) to come up with its classification.According the 3D CNN is attention driven, in that it proceeds byidentifying 3D spatio-temporal bounding boxes as Regions of Interest(RoI) and focusses on them to classify actions.

In one implementation, the input to the deep learning unit 200 caninclude multiple data streams. In one instance, a video sensor signal,which includes red, green and blue data streams, can comprise threechannels. Depth image data can comprise another channel. Additionalchannels can accrue from temperature, sound, vibration, data fromsensors (e.g., torque from a screwdriver) and the like. From the RGB anddepth streams, dense optical flow fields can be computed by the denseoptical flow computation unit 210 and fed to the Convolution NeuralNetworks (CNNs) 220. The RGB and depth streams can also be fed to theCNNs 220 as additional streams of derived data.

The Long Short Term Memory (LSTM) Recurrent Neural Network (RNN) 230 canbe fed the digests from the output of the Convolution Neural Networks(CNNs) 220. The LSTM can essentially be a sequence identifier that istrained to recognize temporal sequences of sub-events that constitute anaction. The combination of the CNNs and LSTM can be jointly trained,with full back-propagation, to recognize low-level actions. Thelow-level actions can be referred to as atomic actions, like picking ascrew, picking a screwdriver, attaching screw to screwdriver and thelike. The Finite State Automata (FSA) 240 can be mathematical models ofcomputations that include a set of state and a set of rules that governthe transition between the states based on the provided input. The FSA240 can be configured to recognize higher-level actions 260 from theatomic actions. The high-level actions 260 can be referred to asmolecular actions, for example turning a screw to affix a hard drive toa computer chassis. The CNNs and LSTM can be configured to performsupervised training on the data from the multiple sensor streams. In oneimplementation, approximately 12 hours of data, collected over thecourse of several days, can be utilized to train the CNNs and LSTMcombination.

Referring now to FIG. 3, an exemplary Convolution Neural Networks (CNNs)and Long Short Term Memory (LSTM) Recurrent Neural Network (RNN), inaccordance with aspects of the present technology, is shown. The CNNscan include a frame feature extractor 310, a first Fully Connected (FC)layer 320, a Region of Interest (RoI) detector unit 330, a RoI poolingunit 340, and a second Fully Connected (FC) layer 350. The operation ofthe CNNs and LSTM will be further explained with reference to FIG. 4,which shows an exemplary method of detecting actions in a sensor stream.

The frame feature extractor 310 of the Convolution Neural Networks(CNNs) 220 can receive a stream of frame-based sensor data, at 410. At420, the frame feature extractor 310 can perform a two-dimensionalconvolution operation on the received video frame and generate atwo-dimensional array of feature vectors. The frame feature extractor310 can work on the full resolution image, wherein a deep network iseffectively sliding across the image generating a feature vector at eachstride position. Thus, each element of the 2D feature vector array is adescriptor for the corresponding receptive field (e.g., fixed portion ofthe underlying image). The first Fully Connected (FC) layer can flattenthe high-level features extracted by the frame feature extractor 310,and provide additional non-linearity and expressive power, enabling themachine to learn complex non-linear combinations of these features.

At 430, the RoI detector unit 330 can combine neighboring featurevectors to make a decision on whether the underlying receptive fieldbelongs to a Region of Interest (RoI) or not. If the underlyingreceptive field belongs to a RoI, a RoI rectangle can be predicted fromthe same set of neighboring feature vectors, at 440. At, 450, a RoIrectangle with a highest score can be chosen by the RoI detector unit330. For the chosen RoI rectangle, the feature vectors lying within itcan be aggregated by the RoI pooling unit 340, at 460. The aggregatedfeature vector is a digest/descriptor for the foreground for that videoframe.

In one implementation, the RoI detector unit 330 can determine a staticRoI. The static RoI identifies a Region of Interest (RoI) within anaggregate set of feature vectors describing a video frame, and generatesa RoI area for the identified RoI. A RoI area within a video frame canbe indicated with an RoI rectangle that encompasses an area of the videoframe designated for action recognition, such as an area in whichactions are performed in a process. Alternatively, the RoI area can bedesignated with a box, circle, highlighted screen, or any othergeometric shape or indicator having various scales and aspect ratiosused to encompass a RoI. The area within the RoI rectangle is the areawithin the video frame to be processed by the Long Short Term Memory(LSTM) for action recognition.

The Long Short Term Memory (LSTM) can be trained using a RoI rectanglethat provides, both, adequate spatial context within the video frame torecognize actions and independence from irrelevant portions of the videoframe in the background. The trade-off between spatial context andbackground independence ensures that the static RoI detector can provideclues for the action recognition while avoiding spurious unreliablesignals within a given video frame.

In another implementation, the RoI detector unit 330 can determine adynamic RoI. A RoI rectangle can encompass areas within a video frame inwhich an action is occurring. By focusing on areas in which actionoccurs, the dynamic RoI detector enables recognition of actions outsideof a static RoI rectangle while relying on a smaller spatial context, orlocal context, than that used to recognize actions in a static RoIrectangle.

In one implementation, the RoI pooling unit 340 extracts a fixed-sizedfeature vector from the area within an identified RoI rectangle, anddiscards the remaining feature vectors of the input video frame. Thefixed-sized feature vector, or foreground feature, includes the featurevectors generated by the video frame feature extractor that are locatedwithin the coordinates indicating a RoI rectangle as determined by theRoI detector unit 330. Because the RoI pooling unit 340 discards featurevectors not included within the RoI rectangle, the Convolution NeuralNetworks (CNNs) 220 analyzes actions within the RoI only, thus ensuringthat unexpected changes in the background of a video frame are noterroneously analyzed for action recognition.

In one implementation, the Convolution Neural Networks (CNNs) 220 can bean Inception ResNet. The Inception ResNet can utilize a sliding windowstyle operation. Successive convolution layers output a feature vectorat each point of a two-dimensional grid. The feature vector at location(x,y) at level l can be derived by weighted averaging features from asmall local neighborhood (aka receptive field) N around the (x,y) atlevel l-1 followed by a pointwise non-linear operator. The non-linearoperator can be the RELU (max(0,x)) operator.

In the sliding window, there can be many more than 7×7 points at theoutput of the last convolution layer. A Fully Connected (FC) convolutioncan be taken over the feature vectors from the 7×7 neighborhoods, whichis nothing but applying one more convolution. The corresponding outputrepresents the Convolution Neural Networks (CNNs) output at the matching224×224 receptive field on the input image. This is fundamentallyequivalent to applying the CNNs to each sliding window stop. However, nocomputation is repeated, thus keeping the inferencing computation costreal time on Graphics Processing Unit (GPU) based machines.

The convolution layers can be shared between RoI detector 330 and thevideo frame feature extractor 310. The RoI detector unit 330 canidentify the class independent rectangular region of interest from thevideo frame. The video frame feature extractor can digest the videoframe into feature vectors. The sharing of the convolution layersimproves efficiency, wherein these expensive layers can be run once perframe and the results saved and reused.

One of the outputs of the Convolution Neural Networks (CNNs) is thestatic rectangular Region of Interest (RoI). The term “static” as usedherein denotes that the RoI does not vary greatly from frame to frame,except when a scene change occurs, and it is also independent of theoutput class.

A set of concentric anchor boxes can be employed at each sliding windowstop. In one implementation, there can be nine anchor boxes per slidingwindow stop for combinations of 3 scales and 3 aspect ratios. Therefore,at each sliding window stop there are two set of outputs. The first setof outputs can be a Region of Interest (RoI) present/absent thatincludes 18 outputs of the form 0 or 1. An output of 0 indicates theabsence of a RoI within the anchor box, and an output of 1 indicates thepresence of a RoI within the anchor box. The second set of outputs caninclude Bounding Box (BBox) coordinates including 36 floating pointoutputs indicating the actual BBox for each of the 9 anchor boxes. TheBBox coordinates are to be ignored if the RoI present/absent outputindicates the absence of a RoI.

For training, sets of video frames with a per-frame Region of Interest(RoI) rectangle are presented to the network. In frames without a RoIrectangle, a dummy 0×0 rectangle can be presented. The Ground Truth forindividual anchor boxes can be created via the Intersection over Union(IoU) of rectangles. For the i_(th) anchor box {right arrow over(b_(l))}={x_(i), y_(i), w_(i), h_(i)} the derived Ground Truth for theRoI presence probability can be determined by Equation 1:

$p_{i}^{*} = \left\{ \begin{matrix}\begin{matrix}1 & {{{IoU}\left( {{\overset{\rightarrow}{b}}_{i},\overset{\rightarrow}{g}} \right)}>=0.7} \\0 & {{{IoU}\left( {{\overset{\rightarrow}{b}}_{i},\overset{\rightarrow}{g}} \right)}<=0.1}\end{matrix} \\{{box}\mspace{14mu} {unused}\mspace{14mu} {for}\mspace{14mu} {training}}\end{matrix} \right.$

where {right arrow over (g)}={x_(g), y_(g), w_(g), h_(g)} is the GroundTruth RoI box for the entire frame.

The loss function can be determined by Equation 2:

${L\left( {p_{i},p_{i}^{*},{\overset{\rightarrow}{b}}_{i},\overset{\rightarrow}{g}} \right)} = {\sum\limits_{i}\; {- {\quad{p_{i}^{*}\log \; p_{i}\mspace{11mu} \left( {{S\left( {x_{i} - x_{g}} \right)} + {\quad\left. \quad{{{\quad\quad}{S\left( {y_{i} - y_{g}} \right)}} + {S\left( {w_{i} - w_{g}} \right)} + {S\left( {h_{i} - h_{g}} \right)}} \right)}} \right.}}}}$

where p_(i) is the predicted probability for presence of Region ofInterest (RoI) in the i_(th) anchor box and the smooth loss function canbe defined by Equation 3:

${S(x)} = \left\{ \begin{matrix}{0.5x^{2}} & {{x} < 1} \\{{x} - 0.5} & {otherwise}\end{matrix} \right.$

The left term in the loss function is the error in predicting theprobability of the presence of a RoI, while the second term is themismatch in the predicted Bounding Box (BBox). It should be noted thatthe second term vanishes when the ground truth indicates that there isno RoI in the anchor box.

The static Region of Interest (RoI) is independent of the action class.In another implementation, a dynamic Region of Interest (RoI), that isclass dependent, is proposed by the CNNs. This takes the form of arectangle enclosing the part of the image where the specific action isoccurring. This increases the focus of the network and takes it a stepcloser to a local context-based action recognition.

Once a Region of Interest (RoI) has been identified, the frame featurecan be extracted from within the RoI. These will yield a backgroundindependent frame digest. But this feature vector also needs to be afixed size so that it can be fed into the Long Short Term Memory (LSTM).The fixed size can be achieved via RoI pooling. For RoI pooling, the RoIcan be tiled up into 7×7 boxes. The mean of all feature vectors within atile can then be determined. Thus, 49 feature vectors that areconcatenated from the frame digest can be produced. The second FullyConnected (FC) layer 350 can provide additional non-linearity andexpressive power to the machine, creating a fixed size frame digest thatcan be consumed by the LSTM 230.

At 470, successive foreground features can be fed into the Long ShortTerm Memory (LSTM) 230 to learn the temporal pattern. The LSTM 230 canbe configured to recognize patterns in an input sequence. In videoaction recognition, there could be patterns within sequences of framesbelonging to a single action, referred to as intra action patterns.There could also be patterns within sequences of actions, referred to asinter action patterns. The LSTM can be configured to learn both of thesepatterns, jointly referred to as temporal patterns. The Long Short TermMemory (LSTM) analyzes a series of foreground features to recognizeactions belonging to an overall sequence. In one implementation, theLSTM outputs an action class describing a recognized action associatedwith an overall process for each input it receives. In anotherimplementation, each class action is comprised of sets of actionsdescribing actions associated with completing an overall process. Eachaction within the set of actions can be assigned a score indicating alikelihood that the action matches the action captured in the inputvideo frame. Each action may be assigned a score such that the actionwith the highest score is designated the recognized action class.

Foreground features from successive frames can be feed into the LongShort Term Memory (LSTM). The foreground feature refers to theaggregated feature vectors from within the Region of Interest (RoI)rectangles. The output of the LSTM at each time step is the recognizedaction class. The loss for each individual frame is the cross entropysoftmax loss over the set of possible action classes. A batch is definedas a set of three randomly selected set of twelve frame sequences in thevideo stream. The loss for a batch is defined as the frame loss averagedover the frame in the batch. The numbers twelve and three are choseempirically. The overall LSTM loss function is given by Equation 4:

${L\left( {B,\left\{ {S_{1},S_{2},\ldots \mspace{11mu},S_{B}} \right\}} \right)} = {\sum\limits_{k = 1}^{B}\; {\sum\limits_{t = 1}^{S_{k}}\; {\sum\limits_{i = 1}^{A}\; {{- \left( \frac{e^{a_{t_{i}}}}{\sum_{j = 1}^{A}\; e^{a_{t_{ij}}}} \right)}\mspace{11mu} \log \mspace{11mu} a_{t_{i}}^{*}}}}}$

where B denotes a batch of ∥B∥ frame sequences {S₁, S₂, . . . ,S_(∥B∥)}. S_(k) comprises a sequence ∥S_(k)∥ frames, wherein in thepresent implementation ∥B∥=3 and ∥S_(k)∥=12k. A denotes the set of allaction classes, a_(t) _(i) denotes the i_(th) action class score for thet_(th) frame from LSTM and a_(t) _(i) ^(*) denotes the correspondingGround Truth.

Referring again to FIG. 1, the machine learning back-end unit 135 canutilize custom labelling tools with interfaces optimized for labelingRoI, cycles and action. The labelling tools can include both standaloneapplication built on top of Open source Computer Vision (OpenCV) and webbrowser application that allow for the labeling of video segment.

Referring now to FIG. 5, an action recognition and analytics system, inaccordance with aspect of the present technology, is shown. Again, theaction recognition and analytics system 500 can be deployed in amanufacturing, health care, warehousing, shipping, retail, restaurant,or similar context. The system 500 similarly includes one or moresensors 505-515 disposed at one or more stations, one or more machinelearning back-end units 520, one or more analytics units 525, and one ormore front-end units 530. The one or more sensors 505-515 can be coupledto one or more local computing devices 535 configured to aggregate thesensor data streams from the one or more sensors 505-515 fortransmission across one or more communication links to a streaming mediaserver 540. The streaming media server 540 can be configured to receivedone or more streams of sensor data from the one or more sensors 505-515.A format converter 545 can be coupled to the streaming media server 540to receive the one or more sensor data streams and convert the sensordata from one format to another. For example, the one or more sensorsmay generate Motion Picture Expert Group (MPEG) formatted (e.g., H.264)video sensor data, and the format converter 545 can be configured toextract frames of JPEG sensor data. An initial stream processor 550 canbe coupled to the format convert 555. The initial stream processor 550can be configured to segment the sensor data into pre-determined chucks,subdivide the chunks into key frame aligned segment, and create persegment sensor data in one or more formats. For example, the initialstream processor 550 can divide the sensor data into five minute chunks,subdivide the chunks into key frame aligned segment, and convert the keyframe aligned segments into MPEG, MPEG Dynamic Adaptive Streaming overHypertext Transfer Protocol (DASH) format, and or the like. The initialstream processor 550 can be configured to store the sensor streamsegments in one or more data structures for storing sensor streams 555.In one implementation, as sensor stream segments are received, each newsegment can be appended to the previous sensor stream segments stored inthe one or more data structures for storing sensor streams 555.

A stream queue 560 can also be coupled to the format converter 545. Thestream queue 560 can be configured to buffer the sensor data from theformat converter 545 for processing by the one or more machine learningback-end units 520. The one or more machine learning back-end units 520can be configured to recognize, in real time, one or more cycles,processes, actions, sequences, objects, parameters and the like in thesensor streams received from the plurality of sensors 505-515. Referringnow to FIG. 6, an exemplary method of detecting actions, in accordancewith aspects of the present technology, is shown. The action recognitionmethod can include receiving one or more sensor streams from one or moresensors, at 610. In one implementation, one or more machine learningback-end units 520 can be configured to receive sensor streams fromsensors 505-515 disposed at one or more stations.

At 620, a plurality of processes including one or more actions arrangedin one or more sequences and performed on one or more objects, and oneor more parameters can be detected. in the one or more sensor streams.At 630, one or more cycles of the plurality of processes in the sensorstream can also be determined. In one implementation, the one or moremachine learning back-end units 520 can recognize cycles, processes,actions, sequences, objects, parameters and the like in sensor streamsutilizing deep learning, decision tree learning, inductive logicprogramming, clustering, reinforcement learning, Bayesian networks, andor the like.

At 640, indicators of the one or more cycles, one or more processes, oneor more actions, one or more sequences, one or more objects, and one ormore parameters can be generated. In one implementation, the one or moremachine learning back-end units 520 can be configured to generateindicators of the one or more cycles, processes, actions, sequences,objects, parameters and or the like. The indicators can includedescriptions, identifiers, values and or the like associated with thecycles, processes, actions, sequences, objects, and or parameters. Theparameters can include, but is not limited to, time, duration, location(e.g., x, y, z, t), reach point, motion path, grid point, quantity,sensor identifier, station identifier, and bar codes.

At 650, the indicators of the one or more cycles, one or more processes,one or more actions, one or more sequences, one or more objects, and oneor more parameters indexed to corresponding portions of the sensorstreams can be stored in one or more data structures for storing datasets 565. In one implementation, the one or more machine learningback-end units 520 can be configured to store a data set including theindicators of the one or more processes, one or more actions, one ormore sequences, one or more objects, and one or more parameters for eachcycle. The data sets can be stored in one or more data structures forstoring the data sets 565. The indicators of the one or more cycles, oneor more processes, one or more actions, one or more sequences, one ormore objects, and one or more parameters in the data sets can be indexedto corresponding portion of the sensor streams in one or more datastructures for storing sensor streams 555.

In one implementation, the one or more streams of sensor data and theindicators of the one or more of the plurality of cycles, one or moreprocesses, one or more actions, one or more sequences, one or moreobjects and one or more parameters indexed to corresponding portion ofthe one or more streams of sensor data can be encrypted when stored toprotect the integrity of the streams of sensor data and or the datasets. In one implementation, the one or more streams of sensor data andthe indicators of the one or more of the plurality of cycles, one ormore processes, one or more actions, one or more sequences, one or moreobjects and one or more parameters indexed to corresponding portion ofthe one or more streams of sensor data can be stored utilizing blockchaining. The blockchaining can be applied across the cycles, sensorstreams, stations, supply chain and or the like. The blockchaining caninclude calculating a cryptographic hash based on blocks of the datasets and or blocks of the streams of sensor data. The data sets, streamsof sensor data and the cryptographic hash can be stored in one or moredata structures in a distributed network.

Referring again to FIG. 5, the one or more analytics units 525 can becoupled to the one or more data structures for storing the sensorstreams 555, one or more data structures for storing the data set 565,one or more additional sources of data 570, one or more data structuresfor storing analytics 575. The one or more analytics units 525 can beconfigured to perform statistical analysis on the cycle, process,action, sequence, object and parameter data in one or more data sets.The one or more analytics units 525 can also utilize additional datareceived from one or more additional data sources 570. The additionaldata sources 570 can include, but are not limited to, ManufacturingExecution Systems (MES), warehouse management system, or patientmanagement system, accounting systems, robot datasheets, human resourcerecords, bill of materials, and sales systems. Some examples of datathat can be received from the additional data sources 570 can include,but is not limited to, time, date, shift, day of week, plant, factory,assembly line, sub-assembly line, building, room, supplier, work space,action capability, and energy consumption, ownership cost. The one ormore analytics units 525 can be configured to utilize the additionaldata from one or more additional source of data 570 to update, correct,extend, augment or the like, the data about the cycles, processes,action, sequences, objects and parameters in the data sets. Similarly,the additional data can also be utilized to update, correct, extend,augment or the like, the analytics generate by the one or more analyticsfront-end units 525. The one or more analytics units 525 can also storetrends and other comparative analytics utilizing the data sets and orthe additional data, can use sensor fusion to merge data from multiplesensors, and other similar processing and store the results in the oneor more data structures for storing analytics 575. In oneimplementation, one or more engines 170, such as the one or more machinelearning back-end units 520 and or the one or more analytics units 525,can create a data structure including a plurality of data sets, the datasets including one or more indicators of at least one of one or morecycles, one or more processes, one or more actions, one or moresequences, one or more object and one or more parameters. The one ormore engine 170 can build the data structure based on the one of one ormore cycles, one or more processes, one or more actions, one or moresequences, one or more object and one or more parameters detected in theone or more sensor streams. The data structure definition, configurationand population can be performed in real time based upon the content ofthe one or more sensor streams. For example, Table 1 shows a tabledefined, configured and populated as the sensor streams are processed bythe one or more machine learning back-end unit 520.

TABLE 1 ENTITY ID DATA STUCTURE (TABLE 1) MOTHER- FRAME HUMAN HAND ARMLEG BOARD SCREW 1 Yes Yes Yes Yes YES Yes 2 Yes No No Yes Yes No 3 YesYes Yes Yes YES YesThe data structure creation process can continue to expand upon theinitial structure and or create additional data structures base uponadditional processing of the one or more sensor streams.

In one embodiment, the status associated with entities is added to adata structure configuration (e.g., engaged in an action, subject to aforce, etc.) based upon processing of the access information. In oneembodiment, activity associated with the entities is added to a datastructure configuration (e.g., engaged in an action, subject to a force,etc.) based upon processing of the access information. One example ofentity status data set created from processing of above entity ID dataset (e.g., motion vector analysis of image object, etc.) is illustratedin Table 2.

TABLE 2 ENTITY STATUS DATA STRUCTURE HAND ARM LEG HUMAN FRAME MOVINGMOVING MOVING MOVING 1 Yes Yes No Yes 2 No No Yes No 3 Yes Yes Yes YesIn one embodiment, a third-party data structure as illustrated in Table3 can be accessed.

TABLE 3 OSHA DATA STRUCTURE SAFE TO SAFE TO ACTIVITY MOVE LEG MOVE HANDSCREWING TO No Yes MOTHERBOARD LIFTING Yes Yes HOUSINGIn one embodiment, activity associated with entities is added to a datastructure configuration (e.g., engaged in an action, subject to a force,etc.) based upon processing of the access information as illustrated inTable 4.

TABLE 4 ACTIVITY DATA STRUCTURE MOTHER- SCREWING TO HUMAN ACTION BOARDFRAME MOTHERBOARD SAFE COMPLETE 1 Yes Yes Yes 2 No NA NO 3 Yes NO YesTable 4 is created by one or more engines 170 based on furtheranalytics/processing of info in Table 1, Table 2 and Table 3. In oneexample, Table 4 is automatically configured to have a column forscrewing to motherboard. In frames 1 and 3 since hand is moving (seeTable 2) and screw present (see Table 1), then screwing to motherboard(see Table 3). In frame 2, since hand is not moving (see Table 2) andscrew not present (see Table 1), then no screwing to motherboard (seeTable 3).

Table 4 is also automatically configured to have a column for humanaction safe. In frame 1 since leg not moving in frame (see Table 2) theworker is safely (see Table 3) standing at workstation while engage inactivity of screwing to motherboard. In frame 3 since leg moving (seeTable 2) the worker is not safely (see Table 3) standing at workstationwhile engage in activity of screwing to motherboard.

The one or more analytics units 525 can also be coupled to one or morefront-end units 580. The one or more front-end units 575 can include amentor portal 580, a management portal 585, and other similar portals.The mentor portal 550 can be configured for presenting feedbackgenerated by the one or more analytics units 525 and or the one or morefont-end units 575 to one or more actors. For example, the mentor portal580 can include a touch screen display for indicating discrepancies inthe processes, actions, sequences, objects and parameters at acorresponding station. The mentor portal 580 could also present trainingcontent generated by the one or more analytics units 525 and or the oneor more front-end units 575 to an actor at a corresponding station. Themanagement port 585 can be configured to enable searching of the one ormore data structures storing analytics, data sets and sensor streams.The management port 585 can also be utilized to control operation of theone or more analytics units 525 for such functions as generatingtraining content, creating work charts, performing line balancinganalysis, assessing ergonomics, creating job assignments, performingcausal analysis, automation analysis, presenting aggregated statistics,and the like.

The action recognition and analytics system 500 can non-intrusivelydigitize processes, actions, sequences, objects, parameters and the likeperformed by numerous entities, including both humans and machines,using machine learning. The action recognition and analytics system 500enables human activity to be measured automatically, continuously and atscale. By digitizing the performed processes, actions, sequences,objects, parameters, and the like, the action recognition and analyticssystem 500 can optimize manual and/or automatic processes. In oneinstance, the action recognition and analytics system 500 enables thecreation of a fundamentally new data set of human activity. In anotherinstance, the action recognition and analytics system 500 enables thecreation of a second fundamentally new data set of man and machinecollaborating in activities. The data set from the action recognitionand analytics system 500 includes quantitative data, such as whichactions were performed by which person, at which station, on whichspecific part, at what time. The data set can also include judgementsbased on performance data, such as does a given person perform better orworse that average. The data set can also include inferences based on anunderstanding of the process, such as did a given product exited theassembly line with one or more incomplete tasks.

Referring now to FIG. 7, an action recognition and analytics system, inaccordance with aspects of the present technology, is shown. The actionrecognition and analytics system can include a plurality of sensorlayers 702, a first Application Programming Interface (API) 704, aphysics layer 706, a second API 708, a plurality of data 710, a thirdAPI 712, a plurality of insights 714, a fourth API 716 and a pluralityof engine layers 718. The sensor layer 702 can include, for example,cameras at one or more stations 720, MES stations 722, sensors 724, IIoTintegrations 726, process ingestion 728, labeling 730, neural networktraining 732 and or the like. The physics layer 706 captures data fromthe sensor layer 702 to passes it to the data layer 710. The data layer710, can include but not limited to, video and other streams 734, +NNannotations 736, +MES 738, +OSHA database 740, and third-party data 742.The insights layer 714 can provide for video search 744, time seriesdata 746, standardized work 748, and spatio-temporal 842. The enginelayer 718 can be utilized for inspection 752, lean/line balancing 754,training 756, job assignment 758, other applications 760, quality 763,traceability 764, ergonomics 766, and third party applications 768.

Referring now to FIG. 8, an exemplary station, in accordance withaspects of the present technology, is shown. The station 800 is an areasassociated with one or more cycles, processes, actions, sequences,objects, parameters and or the like, herein also referred to asactivity. Information regarding a station can be gathered and analyzedautomatically. The information can also be gathered and analyzed in realtime. In one exemplary implementation, an engine participates in theinformation gathering and analysis. The engine can use ArtificialIntelligence to facilitate the information gathering and analysis. It isappreciated there can be many different types of stations with variousassociated entities and activities. Additional descriptions of stations,entities, activities, information gathering, and analytics are discussedin other sections of this detailed description.

A station or area associated with an activity can include variousentities, some of which participate in the activity within the area. Anentity can be considered an actor, an object, and so on. An actor canperform various actions on an object associated with an activity in thestation. It is appreciated a station can be compatible with varioustypes of actors (e.g., human, robot, machine, etc.). An object can be atarget object that is the target of the action (e.g., thing being actedon, a product, a tool, etc.). It is appreciated that an object can be atarget object that is the target of the action and there can be varioustypes of target objects (e.g., component of a product or article ofmanufacture, an agricultural item, part of a thing or person beingoperated on, etc.). An object can be a supporting object that supports(e.g., assists, facilitates, aids, etc.) the activity. There can bevarious types of supporting objects, including load bearing components(e.g., a work bench, conveyor belt, assembly line, table top etc.), atool (e.g., drill, screwdriver, lathe, press, etc.), a device thatregulates environmental conditions (e.g., heating ventilating and airconditioning component, lighting component, fire control system, etc.),and so on. It is appreciated there can be many different types ofstations with a various entities involved with a variety of activities.Additional descriptions of the station, entities, and activities arediscussed in other sections of this detailed description.

The station 800 can include a human actor 810, supporting object 820,and target objects 830 and 840. In one embodiment, the human actor 810is assembling a product that includes target objects 830, 840 whilesupporting object 820 is facilitating the activity. In one embodiment,target objects 830, 840 are portions of a manufactured product (e.g., amotherboard and a housing of an electronic component, a frame and amotor of a device, a first and a second structural member of anapparatus, legs and seat portion of a chair, etc.). In one embodiment,target objects 830, 840 are items being loaded in a transportationvehicle. In one embodiment, target objects 830, 840 are products beingstocked in a retail establishment. Supporting object 820 is a loadbearing component (e.g., a work bench, a table, etc.) that holds targetobject 840 (e.g., during the activity, after the activity, etc.). Sensor850 senses information about the station (e.g., actors, objects,activities, actions, etc.) and forwards the information to one or moreengines 860. Sensor 850 can be similar to sensor 135. Engine 860 caninclude a machine learning back end component, analytics, and front endsimilar to machine learning back end unit 180, analytics unit 190, andfront end 190. Engine 860 performs analytics on the information and canforward feedback to feedback component 870 (e.g., a display, speaker,etc.) that conveys the feedback to human actor 810.

Referring now to FIG. 9, an exemplary station, in accordance withaspects of the present technology, is shown. The station 900 includes arobot actor 910, target objects 920, 930, and supporting objects 940,950. In one embodiment, the robot actor 910 is assembling target objects920, 930 and supporting objects 940, 950 are facilitating the activity.In one embodiment, target objects 920, 930 are portions of amanufactured product. Supporting object 940 (e.g., an assembly line, aconveyor belt, etc.) holds target objects 920, 930 during the activityand moves the combined target object 920, 930 to a subsequent station(not shown) after the activity. Supporting object 940 provides areasupport (e.g., lighting, fan temperature control, etc.). Sensor 960senses information about the station (e.g., actors, objects, activities,actions, etc.) and forwards the information to engine 970. Engine 970performs analytics on the information and forwards feedback to acontroller 980 that controls robot 910. Engine 970 can be similar toengine 170 and sensor 960 can be similar to sensor 135.

A station can be associated with various environments. The station canbe related to an economic sector. A first economic sector can includethe retrieval and production of raw materials (e.g., raw food, fuel,minerals, etc.). A second economic sector can include the transformationof raw or intermediate materials into goods (e.g., manufacturingproducts, manufacturing steel into cars, manufacturing textiles intoclothing, etc.). A third sector can include the supply and delivery ofservices and products (e.g., an intangible aspect in its own right,intangible aspect as a significant element of a tangible product, etc.)to various parties (e.g., consumers, businesses, governments, etc.). Inone embodiment, the third sector can include sub sectors. One sub sectorcan include information and knowledge-based services. Another sub sectorcan include hospitality and human services. A station can be associatedwith a segment of an economy (e.g., manufacturing, retail, warehousing,agriculture, industrial, transportation, utility, financial, energy,healthcare, technology, etc,). It is appreciated there can be manydifferent types of stations and corresponding entities and activities.Additional descriptions of the station, entities, and activities arediscussed in other sections of this detailed description.

In one embodiment, station information is gathered and analyzed. In oneexemplary implementation, an engine (e.g., an information processingengine, a system control engine, an Artificial Intelligence engine,etc.) can access information regarding the station (e.g., information onthe entities, the activity, the action, etc.) and utilizes theinformation to perform various analytics associated with the station. Inone embodiment, engine can include a machine learning back end unit,analytics unit, front end unit, and data storage unit similar to machinelearning back end 180, analytics 185, front end 190 and data storage175. In one embodiment, a station activity analysis process isperformed. Referring now to FIG. 10, an exemplary station activityanalysis method, in accordance with one embodiment, is shown.

At 1010, information regarding the station is accessed. In oneembodiment, the information is accessed by an engine. The informationcan be accessed in real time. The information can be accessed frommonitors/sensors associated with a station. The information can beaccessed from an information storage repository. The information caninclude various types of information (e.g., video, thermal, optical,etc.). Additional descriptions of the accessing information arediscussed in other sections of this detailed description

At 1020, information is correlated with entities in the station andoptionally with additional data sources. In one embodiment, theinformation the correlation is established at least in part by anengine. The engine can associate the accessed information with an entityin a station. An entity can include an actor, an object, and so on.Additional descriptions of the correlating information with entities arediscussed in other sections of this detailed description.

At 1030, various analytics are performed utilizing the accessedinformation at 1010, and correlations at 1020. In one embodiment, anengine utilizes the information to perform various analytics associatedwith station. The analytics can be directed at various aspects of anactivity (e.g., validation of actions, abnormality detection, training,assignment of actor to an action, tracking activity on an object,determining replacement actor, examining actions of actors with respectto an integrated activity, automatic creation of work charts, creatingergonomic data, identify product knitting components, etc.) Additionaldescriptions of the analytics are discussed in other sections of thisdetailed description.

At 1040, optionally, results of the analysis can be forwarded asfeedback. The feedback can include directions to entities in thestation. In one embodiment, the information accessing, analysis, andfeedback are performed in real time. Additional descriptions of thestation, engine, entities, activities, analytics and feedback arediscussed in other sections of this detailed description,

It is also appreciated that accessed information can include generalinformation regarding the station (e.g., environmental information,generic identification of the station, activities expected in station, agolden rule for the station, etc.). Environmental information caninclude ambient aspects and characteristics of the station (e.g.,temperature, lighting conditions, visibility, moisture, humidity,ambient aroma, wind, etc.).

It also appreciated that some of types of characteristics or featurescan apply to a particular portion of a station and also the generalenvironment of a station. In one exemplary implementation, a portion ofa station (e.g., work bench, floor area, etc.) can have a firstparticular visibility level and the ambient environment of the stationcan have a second particular visibility level. It is appreciated thatsome of types of characteristics or features can apply to a particularentity in a station and also the station environment. In one embodiment,an entity (e.g., a human, robot, target object etc.) can have a firstparticular temperature range and the station environment can have asecond particular temperature range.

The action recognition and analytics system 100, 500 can be utilized forprocess validation, anomaly detection and/or process quality assurancein real time. The action recognition and analytics system 100, 500 canalso be utilized for real time contextual training. The actionrecognition and analytics system 100, 500 can be configured forassembling training libraries from video clips of processes to speed newproduct introductions or onboard new employees. The action recognitionand analytics system 100, 500 can also be utilized for line balancing byidentifying processes, sequences and/or actions to move among stationsand implementing lean processes automatically. The action recognitionand analytics system 100, 500 can also automatically create standardizedwork charts by statistical analysis of processes, sequences and actions.The action recognition and analytics system 100, 500 can alsoautomatically create birth certificate videos for a specific unit. Theaction recognition and analytics system 100, 500 can also be utilizedfor automatically creating statistically accurate ergonomics data. Theaction recognition and analytics system 100, 500 can also be utilized tocreate programmatic job assignments based on skills, tasks, ergonomicsand time. The action recognition and analytics system 100, 500 can alsobe utilized for automatically establishing traceability including forcausal analysis. The action recognition and analytics system 100, 500can also be utilized for kitting products, including real timeverification of packing or unpacking by action and image recognition.The action recognition and analytics system 100, 500 can also beutilized to determine the best robot to replace a worker when ergonomicproblems are identified. The action recognition and analytics system100, 500 can also be utilized to design an integrated line of humans andcobot and/or robots. The action recognition and analytics system 100,500 can also be utilized for automatically programming robots based onobserving non-modeled objects in the work space.

Referring now to FIGS. 11A and 11B, a method of creating work charts, inaccordance with aspects of the present technology, is shown. The methodcan include receiving one or more given indicators or criteria, at 1110.In one implementation, the one or more engines 170 can receive one ormore indicators or criteria, such as a given time period, an averageduration, a fasters duration for the cycles or for a given task, aslowest duration for the cycles or a given task, a distribution, a lineidentifier, a worker identifier, a distribution of a sequence and or thelike.

At 1120, one or more given data sets can be accessed based on the one ormore given indicators or criteria. The data sets can include one or moreindicators of at least one of one or more cycles, one or more processes,one or more actions, one or more sequences, one or more objects, and oneor more parameters in one or more sensor streams for a subject. Thesubject can include an article of manufacture, a health care service,warehousing transaction, a shipping transaction, a retail transaction,or the like. In one implementation, the one or more indicators of atleast one of one or more cycles, one or more processes, one or moreactions, one or more sequences, one or more objects, and one or moreparameters of the data sets can be indexed to corresponding portions ofone or more sensor streams. In one implementation, one or more engines170 can be configured to access one or more given data sets stored inone or more data structures on one or more data storage units 175. Inone implementation, the data sets and the corresponding portions of oneor more sensor streams can be blockchained to protect the integrity ofthe data. The blockchaining can be applied across the cycles, sensorstreams, stations, supply chain and or the like.

At 1130, a representative data set for the subject can be determinedfrom the one or more given data sets based on the one or more givenindicators or criteria. The representative data set can include one ormore indicators of at least one of one or more cycles, one or moreprocesses, one or more actions, one or more sequences, one or moreobjects, and one or more parameters of the subject satisfying one ormore of the given parameters or criteria. In one implementation, the oneor more engines 170 can be configured to statistically analyze the oneor more given data sets to determine the representative data set for thesubject.

At 1140, a work chart for the subject can be created from therepresentative data set. The work chart can include a plurality of workelements and dependencies between the work elements. In oneimplementation, the one or more engines 170 can be configured to createthe work chart from the one or more indicators of at least one of one ormore cycles, one or more processes, one or more actions, one or moresequences, one or more objects, and one or more parameters of therepresentative data set. In one implementation, the plurality of workelements and dependencies between the work elements can be indexed tothe corresponding indicators of the at least one of one or more cycles,one or more processes, one or more actions, one or more sequences, oneor more objects, and one or more parameters of the representative dataset.

At 1150, the work chart can be output. In one implementation, the one ormore engines 170 can be configured to out the work chart in a graphicsuser interface for display to a user. In one implementation, the workchart can be created for any, every or a selection of cycles. Forexample, a work chart can be created for a selection of cycles in agiven time period specified by the received one or more given indicatorsor criteria. In another example, a work chart can be created for everysequence detected in the one or more sensor streams. In another example,a chart can be created based on an average duration of cycles,processes, sequences and or actions. In yet another example, a chart canbe created for a fastest or slowest possible duration for a cycle oreach task. In addition, a work chart can be created to show adistribution of different sequences of tasks in a plot. For example, along-tail of a distribution can show that given sequences are followed,whereas equal distribution shows no one sequence is followed by amajority of workers. A most or least efficient sequence performed can beidentified from the distribution. Similarly, a sequence with the mostdeviation can be identified.

Referring now to FIG. 12, an exemplary work chart, in accordance withaspects of the present technology, is shown. As illustrate, the workchart can include a plurality of work element 1210-1230 and dependencies1240, 1250 between the work elements 1210-1230. In one implementation,the work chart can be organized based on a lean framework. The lean workchart can include work that can be characterized as value added work andnonvalue added work. The non-value-added work can include necessary,walking and idle. Value added work can be work which adds value to thesubject. Non-value added, but necessary work, can be work that does notadd value to the end subject but is necessary. A non-value-added walkingwork can be the work of moving between machines or between areas of astation. Non-value-added idle work can be work that neither adds valueto the end subject, nor is necessary to be able to provide the endsubject. Various types of delays, such as late shipments, are example ofnon-value-added idle work. Non-value-added idle work should beeliminated or reduced whenever possible. In accordance with aspects ofthe present technology, the work charts can be based on statistical dataderived from one or more sensor streams over many cycles that capturedata tens of time a second. The resolution, accuracy and breadth of thedata from the one or more sensor streams allow the processes to beconstantly adjusted to gain efficiencies in fields where even a smalldecimal percentage increase can be a big win.

Referring now to FIG. 13, an exemplary work chart, in accordance withaspects of the present technology is shown. The work chart can be astandardized work chart, which illustrates the progress through a cycleof one or more processes, one or more actions, one or more sequences,one or more objects, and or one or more parameters including theassociated time. The horizontal direction can represent the timerequired for tasks and the vertical direction can represent the timebetween tasks. Statistical values, such as mean and standard deviation,can be calculated for each step. These times can then be compared to thetime available to complete a task to meet production requirements(takt). The standardized work chart provides a way to visualize aprocess to determine where and how much improvement can be made.

Referring again to FIGS. 11A and 11B, the work charting method canfurther include receiving a selection of a work element, at 1160. In oneimplementation, the one or more engines 170 can be configured to receivea selection of a given work element of the work chart. For example, auser can click on a given work element in a graphical user interface.

At 1170, one or more indicators of at least one of the one or morecycles, one or more processes, one or more actions, one or moresequences, one or more objects, one or more parameters, and orcorresponding portions of the plurality of sensor streams indexed by theselected model element can be retrieved. In one implementation, theengine 170 can be configured to access one or more data structures inone or more data storage units 175 to retrieve one or more cycles,processes, actions, sequences, objects and or parameters of therepresentative data set indexed by the selected model element. The oneor more engines 170 can also be configured to access one or more datastructures in the one or more data storage units 175 to retrievecorresponding portions of one or more sensor streams indexed by the oneor more cycles, one or more processes, one or more actions, one or moresequences, one or more objects, and one or more parameters of therepresentative data set.

At 1180, the one or more indicators of at least one of the one or moreof the one or more cycles, one or more processes, one or more actions,one or more sequences, one or more objects, one or more parameters, andor corresponding portions of the plurality of sensor streams can beoutput for the selected model element. In one implementation, the one ormore engines 170 can be configured to output the one or more cycles, oneor more processes, one or more actions, one or more sequences, one ormore objects, one or more parameters, or corresponding portions of theplurality of sensor streams in the graphics user interface for displayto the user.

In aspect, the action recognition and analytics method can create a workchart of an entire assembly process, step by step at each station forevery instance of a subject produced. Work charts can also be createdbased on one or more specified indicators and or criteria. The workchart can also be linked to corresponding indicators of cycles,processes, actions, sequences, objects, parameters and or correspondingportions of sensor streams of a representative instance of a subject.

Referring now to FIG. 14, a block diagram of an exemplary computingdevice upon which various aspects of the present technology can beimplemented. In various embodiments, the computer system 1400 mayinclude a cloud-based computer system, a local computer system, or ahybrid computer system that includes both local and remote devices. In abasic configuration, the system 1400 includes at least one processingunit 1402 and memory 1404. This basic configuration is illustrated inFIG. 14 by dashed line 1406. The system 1400 may also have additionalfeatures and/or functionality. For example, the system 1400 may includeone or more Graphics Processing Units (CPUs) 1410. Additionally, thesystem 1400 may also include additional storage (e.g., removable and/ornon-removable) including, but not limited to, magnetic or optical disksor tape. Such additional storage is illustrated in FIG. 14 by removablestorage 1408 and non-removable storage 1420.

The system 1400 may also contain communications connection(s) 1422 thatallow the device to communicate with other devices, e.g., in a networkedenvironment using logical connections to one or more remote computers.Furthermore, the system 1400 may also include input device(s) 1424 suchas, but not limited to, a voice input device, touch input device,keyboard, mouse, pen, touch input display device, etc. In addition, thesystem 1400 may also include output device(s) 1426 such as, but notlimited to, a display device, speakers, printer, etc.

In the example of FIG. 14, the memory 1404 includes computer-readableinstructions, data structures, program modules, and the like associatedwith one or more various embodiments 1450 in accordance with the presentdisclosure. However, the embodiments(s) 1450 may instead reside in anyone of the computer storage media used by the system 1400, or may bedistributed over some combination of the computer storage media, or maybe distributed over some combination of networked computers, but is notlimited to such.

It is noted that the computing system 1400 may not include all of theelements illustrated by FIG. 14. Moreover, the computing system 1400 canbe implemented to include one or more elements not illustrated by FIG.14. It is pointed out that the computing system 1400 can be utilized orimplemented in any manner similar to that described and/or shown by thepresent disclosure, but is not limited to such.

The foregoing descriptions of specific embodiments of the presenttechnology have been presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit theinvention to the precise forms disclosed, and obviously manymodifications and variations are possible in light of the aboveteaching. The embodiments were chosen and described in order to bestexplain the principles of the present technology and its practicalapplication, to thereby enable others skilled in the art to best utilizethe present technology and various embodiments with variousmodifications as are suited to the particular use contemplated. It isintended that the scope of the invention be defined by the claimsappended hereto and their equivalents.

What is claimed is:
 1. A method of creating work, charts comprising:receiving one or more given indicators or criteria; accessing one ormore given data sets based on the one or more given indicators orcriteria, wherein the one or more given data sets include one or moreindicators of at least one of one or more cycles, one or more processes,one or more actions, one or more sequences, one or more objects, and oneor more parameters; determining a representative data set from the oneor more given data sets; and creating a work chart from therepresentative data set, wherein the work chart include a plurality ofwork elements and dependencies between the work elements.
 2. The methodaccording to claim 1, wherein a subject of the work chart comprises anarticle of manufacture, a health care service, a shipping transaction ora retailing transaction.
 3. The method according to claim 1, wherein thework chart is organized based on a lean framework including value addedand non-value added, wherein the non-value added includes idle, walkingand necessary.
 4. The method according to claim 1, wherein the one ormore given data sets further includes the one or more indicators of atleast one of one or more cycles, one or more processes, one or moreactions, one or more sequences, one or more objects, and one or moreparameters indexed to corresponding portions of one or more sensorstreams.
 5. The method according to claim 1, further comprising:receiving a selection of a work element; retrieving one or moreindicators of at least one of the one or more cycles, one or moreprocesses, one or more actions, one or more sequences, one or moreobjects, one or more parameters indexed by the selected model element;and outputting the one or more indicators of the at least one of one ormore of the one or more cycles, one or more processes, one or moreactions, one or more sequences, one or more objects, one or moreparameters, or corresponding portions of the plurality of sensor streamsfor the selected model element.
 6. The method according to claim 1,further comprising: retrieving corresponding portions of the one or moresensor streams indexed by the selected model element; and outputting thecorresponding portions of the one or more sensor streams indexed by theselected model element.
 7. The method according to claim 1, wherein theone or more given data sets and the corresponding portions of one ormore sensor streams are blockchained.
 8. One or more non-transitorycomputing device-readable storage mediums storing instructionsexecutable by one or more computing devices to perform an actionrecognition and analytics method of creating work charts comprising:receiving one or more given indicators or criteria accessing one or moregiven data sets based on the one or more given indicators or criteria,wherein the one or more given data sets include one or more indicatorsof at least one of one or more cycles, one or more processes, one ormore actions, one or more sequences, one or more objects, and one ormore parameters indexed to corresponding portions of one or more sensorstreams; statistically analyzing the one or more given data sets basedon the one or more given indicators or criteria to determine arepresentative data set; and creating a work chart from therepresentative data set, wherein the work chart includes a plurality ofwork element and dependencies between the work elements.
 9. The one ormore non-transitory computing device-readable storage mediums storinginstructions executable by one or more computing devices to perform theaction recognition and analytics method of creating work chartsaccording to claim 8, wherein a subject of the one or more given datasets comprises an article of manufacture, a health care service, ashipping transaction or a retailing transaction.
 10. The one or morenon-transitory computing device-readable storage mediums storinginstructions executable by one or more computing devices to perform theaction recognition and analytics method of creating work chartsaccording to claim 8, wherein the work chart is organized based on alean framework including value added and non-value added.
 11. The one ormore non-transitory computing device-readable storage mediums storinginstructions executable by one or more computing devices to perform theaction recognition and analytics method of creating work chartsaccording to claim 8, wherein the non-value added includes idle, walkingand necessary.
 12. The one or more non-transitory computingdevice-readable storage mediums storing instructions executable by oneor more computing devices to perform the action recognition andanalytics method of creating work charts according to claim 8, furthercomprising: receiving a selection of a work element; and outputting theone or more indicators of the at least one of one or more of the one ormore cycles, one or more processes, one or more actions, one or moresequences, one or more objects, one or more parameters, or correspondingportions of the plurality of sensor streams for the selected modelelement.
 13. The one or more non-transitory computing device-readablestorage mediums storing instructions executable by one or more computingdevices to perform the action recognition and analytics method ofcreating work charts according to claim 12, wherein the one or moreindicators of corresponding ones of the one or more cycles, the one ormore processes, the one or more actions, the one or more sequences, theone or more objects, and the one or more parameters are indexed tocorresponding portions of the plurality of sensor streams bycorresponding time stamps.
 14. A system comprising: one or more datastorage unit; one or more interfaces; one or more engines configured to;receive one or more given indicators or criteria; access one or moregiven data sets stored on the one or more data storage units based onthe one or more given indicators or criteria, wherein the one or moregiven data sets include one or more indicators of at least one of one ormore cycles, one or more processes, one or more actions, one or moresequences, one or more objects, and one or more parameters, indexed tocorresponding portions of one or more sensor streams; determine arepresentative data set from the one or more given data sets; create awork chart from the representative data set, wherein the work chartinclude a plurality of work elements and dependencies between the workelements; and output the work chart in a graphical user interface on theone or more interfaces.
 15. The system of claim 14, wherein the one ormore given data sets and the corresponding portions of one or moresensor streams are blockchained.
 16. The system of claim 14, wherein thesubject of the one or more sensor streams comprises an article ofmanufacture, a health care service, a shipping transaction or aretailing transaction.
 17. The system of claim, 14, wherein theidentifiers of the at least one of one or more cycles, one or moreprocesses, one or more actions, one or more sequences, one or moreobjects and one or more parameters are indexed to corresponding portionsof the one or more sensor streams by corresponding time stamps.
 18. Thesystem of claim 14, wherein the work chart is organized based on a leanframework including value added and non-value added.
 19. The system ofclaim 14, wherein the non-value added includes idle, walking andnecessary.
 20. The system of claim 14, wherein the one or more analyticsfront-end units are further configured to: receive a selection of a workelement; retrieve one or more indicators of at least one of the one ormore cycles, one or more processes, one or more actions, one or moresequences, one or more objects, one or more parameters and thecorresponding portions of one or more sensor streams indexed by theselected model element from the one or more data structures store on theone or more data storage units; and output the one or more indicators ofat least one of one or more cycles, one or more processes, one or moreactions, one or more sequences, one or more objects, one or moreparameters, or corresponding portions of the plurality of sensor streamsfor the selected model element in a graphical user interface on the oneor more interfaces.